6,367 research outputs found
Improving customer churn prediction by data augmentation using pictorial stimulus-choice data
The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of
comparing images based on human pose, avoiding potential challenges of
estimating body joint positions. Pose embedding learning is formulated under a
triplet-based distance criterion. A deep architecture is used to allow learning
of a representation capable of making distinctions between different poses.
Experiments on human pose matching and retrieval from video data demonstrate
the potential of the method
Variational recurrent sequence-to-sequence retrieval for stepwise illustration
We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are
two widely used tools for visual recognition. They are typically viewed as
distinct approaches: DPMs are graphical models (Markov random fields), while
CNNs are "black-box" non-linear classifiers. In this paper, we show that a DPM
can be formulated as a CNN, thus providing a novel synthesis of the two ideas.
Our construction involves unrolling the DPM inference algorithm and mapping
each step to an equivalent (and at times novel) CNN layer. From this
perspective, it becomes natural to replace the standard image features used in
DPM with a learned feature extractor. We call the resulting model DeepPyramid
DPM and experimentally validate it on PASCAL VOC. DeepPyramid DPM significantly
outperforms DPMs based on histograms of oriented gradients features (HOG) and
slightly outperforms a comparable version of the recently introduced R-CNN
detection system, while running an order of magnitude faster
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